Optimal Energy Shaping via Neural Approximators

• URL: http://arxiv.org/abs/2101.05537v1
• Date: Thu, 14 Jan 2021 10:25:58 GMT
• Title: Optimal Energy Shaping via Neural Approximators
• Authors: Stefano Massaroli, Michael Poli, Federico Califano, Jinkyoo Park, Atsushi Yamashita and Hajime Asama
• Abstract summary: We introduce optimal energy shaping as an enhancement of classical passivity-based control methods. A systematic approach to adjust performance within a passive control framework has yet to be developed.
• Score: 16.879710744315233
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce optimal energy shaping as an enhancement of classical passivity-based control methods. A promising feature of passivity theory, alongside stability, has traditionally been claimed to be intuitive performance tuning along the execution of a given task. However, a systematic approach to adjust performance within a passive control framework has yet to be developed, as each method relies on few and problem-specific practical insights. Here, we cast the classic energy-shaping control design process in an optimal control framework; once a task-dependent performance metric is defined, an optimal solution is systematically obtained through an iterative procedure relying on neural networks and gradient-based optimization. The proposed method is validated on state-regulation tasks.

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